Several uses for mobile devices capable of navigating around an environment such as an office or a factory floor have been proposed. Some methods for allowing a mobile device to perform such navigation are known.
One such method is through the use of active beacons installed in the environment in which the mobile device is required to navigate. Active beacons may take several forms, for example transponders buried beneath the floor or radio beacons scattered around a building. A mobile device calculates its position based on the angle of incidence of the signal received from the beacon and the timing data associated with the signal. Timing data of the signal is used to calculate distance of the mobile device to the signal.
A further method for self-navigation of mobile devices is through the use of artificial/natural landmarks. This method requires a very high level of image processing because landmarks must be recognized by the mobile device. Furthermore the mobile device is required to identify moving objects in the environment in order to avoid collisions. In the case of artificial landmarks, the actual surroundings must be modified with artificial landmarks being inserted into the environments. These landmarks will be such that image processing circuitry in the mobile device will be able to easily identify their shape and color. An example of these landmarks is including the tape on the floor which forms a continuous landmark for the mobile device to follow.
Model matching has also been proposed as a self-navigation system. In model matching the mobile device uses sensors to build up a model of the surrounding environment. This model is matched against a global model stored in the mobile device memory to determine the position of the mobile device. This method is process intensive and requires sophisticated image processing capabilities.
Simpler methods for self-navigation involve relative position measurements. In relative position measurements the motion and direction of travel of the mobile device is recorded through for example encoders capturing wheel rotation or steering orientation or gyroscopes and accelerometers. The mobile device then estimates its position based on a start position and these parameters.
Existing visual based self-navigation systems are complicated and processor intensive requiring a high level of image processing capabilities. Active beacons require both a time measurement of the beacon signal and an angle of incidence of the signal which is susceptible to interference and requires a high accuracy in the decoding equipment. Visual systems and to a lesser degree active beacons tend to be expensive. The existing cheaper simpler systems of relative position measurement are less accurate and take into account only the motion of the mobile device itself.